Jack D. Saunders
ABSTRACT
Positive-Unlabeled (PU) learning is a growing field of machine learning that now consists of numerous algorithms; the number is now so large that considering an extensive manual search to select the best algorithm for a given task is impractical. As such, the area of PU learning could benefit from an Automated Machine Learning (Auto-ML) system, which selects the best algorithm for a given input dataset, among a pre-defined set of candidate algorithms. This work proposes such with GA-Auto-PU, a Genetic Algorithm-based Auto-ML system that can generate PU learning algorithms. Experiments with 20 real-world datasets show that GA-Auto-PU significantly outperformed a state-of-the-art PU learning method.
- Bekker, J. and Davis, J., 2020. Learning from positive and unlabeled data: A survey. Machine Learning, 109(4), 719--760.Google Scholar
Digital Library
- Elkan, C. and Noto, K., 2008. Learning classifiers from only positive and unlabeled data. In Proc. 14th ACM SIGKDD Intern. Conf. on Knowledge Discovery and Data Mining, 213--220.Google Scholar
- Li, X. and Liu, B., 2003. Learning to classify texts using positive and unlabeled data. In Proc 18th Int. Joint Conf. on Artif. Intel.. 3, 587--592.Google Scholar
- Zheng, Y., et al., 2019. DDI-PULearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions. BMC Bioinformatics, 20(19), 1--12.Google Scholar
- He, X, Zhao, K. and Chu, X., 2021. AutoML: A Survey of the State-of-the-Art. Knowledge-Based Systems, 212, article 106622.Google Scholar
- Brazdil, P., et al., 2008. Metalearning: Applications to data mining. Springer.Google Scholar
- Olson, R.S., et al., 2016, July. Evaluation of a tree-based pipeline optimization tool for automating data science. In Proc. of the Genetic and Evolutionary Computation Conf. 2016, 485--492.Google ScholarDigital Library
- de Sá, A.G., et al., 2017. RECIPE: a grammar-based framework for automatically evolving classification pipelines. Proc. European Conf. on Genetic Programming, 246--261.Google ScholarCross Ref
- Freitas, A.A., 2004. A critical review of multi-objective optimization in data mining: a position paper. ACM SIGKDD Explorations Newsletter, 6(2), 77--86.Google ScholarDigital Library
- Ellis, P.D., 2010. The essential guide to effect sizes. CUP.Google Scholar
- Asuncion, A., Newman, D., 2007. UCI machine learning repository.Google Scholar
- Marcus, D.S. et al., 2010. Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. Journal of Cognitive Neuroscience, 22(12), 2677--2684.Google ScholarDigital Library
- Pereira, B., et al., 2016. The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes. Nature Communications, 7(1), 1--16.Google Scholar
- Fleming, T.R. and Harrington, D.P., 1991. Counting Processes and Survival Analysis. John Wiley and Sons.Google Scholar
- Islam, M.F., et al, 2020. Likelihood prediction of diabetes at early stage using data mining techniques. In Computer Vision and Machine Intelligence in Medical Image Analysis, 113--125.Google Scholar
- Chicco, D., Jurman, G., 2020. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making, 20(1), 1--16.Google ScholarCross Ref
- Hlavnička, J. et al., 2017. Automated analysis of connected speech reveals early biomarkers of Parkinson's disease in patients with rapid eye movement sleep behaviour disorder. Scientific Reports, 7(1), 1--13.Google ScholarCross Ref
- Emon, M.U., et al. 2020. Performance Analysis of Machine Learning Approaches in Stroke Prediction. In Proc. 4th Intern. Conf. on Electronics, Communic. and Aerospace Technol. (ICECA), 1464--1469.Google ScholarCross Ref
- Zeng, X., et al., 2020. Predicting disease-associated circular RNAs using deep forests combined with positive-unlabeled learning methods. Briefings in Bioinformatics, 21(4), 1425--1436.Google ScholarCross Ref
- Wilcoxon, F., et al., 1963. Critical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank test. Selected tables in mathematical statistics, 1, 171--259.Google Scholar
Index Terms
- GA-auto-PU: a genetic algorithm-based automated machine learning system for positive-unlabeled learning
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